This paper is in the following e-collection/theme issue:

#xCardiovascularMedicine (3) #xHealthInformatics (11) #xSurgery (2) Artificial Intelligence (1371) Machine Learning (49) Cardiac Surgery (14) Clinical Informatics (1022) Decision Support for Health Professionals (1185) Cardiac Risk and Cardiac Risk Calculators (45)

Published on in Vol 5 (2024)

This is a member publication of University of Bristol (Jisc)

Preprints (earlier versions) of this paper are available at https://www.medrxiv.org/content/10.1101/2023.01.21.23284795v1, first published .
Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis

Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis

Performance Drift in Machine Learning Models for Cardiac Surgery Risk Prediction: Retrospective Analysis

Authors of this article:

Tim Dong1 Author Orcid Image ;   Shubhra Sinha1 Author Orcid Image ;   Ben Zhai2 Author Orcid Image ;   Daniel Fudulu1 Author Orcid Image ;   Jeremy Chan1 Author Orcid Image ;   Pradeep Narayan3 Author Orcid Image ;   Andy Judge1 Author Orcid Image ;   Massimo Caputo1 Author Orcid Image ;   Arnaldo Dimagli1 Author Orcid Image ;   Umberto Benedetto1 Author Orcid Image ;   Gianni D Angelini1 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Dong T, Oronti I, Sinha S, Freitas A, Zhai B, Chan J, Fudulu D, Caputo M, Angelini G. Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects. Bioengineering 2024;11(10):1039 View
  2. Sinha S, Dong T, Dimagli A, Judge A, Angelini G. A machine learning algorithm-based risk prediction score for in-hospital/30-day mortality after adult cardiac surgery. European Journal of Cardio-Thoracic Surgery 2024;66(4) View